1,198 research outputs found
Statistical foundations of ecological rationality
If we reassess the rationality question under the assumption that the uncertainty of the natural world is largely unquantifiable, where do we end up? In this article the author argues that we arrive at a statistical, normative, and cognitive theory of ecological rationality. The main casualty of this rebuilding process is optimality. Once we view optimality as a formal implication of quantified uncertainty rather than an ecologically meaningful objective, the rationality question shifts from being axiomatic/probabilistic in nature to being algorithmic/predictive in nature. These distinct views on rationality mirror fundamental and long-standing divisions in statistics
Task-Driven Estimation and Control via Information Bottlenecks
Our goal is to develop a principled and general algorithmic framework for
task-driven estimation and control for robotic systems. State-of-the-art
approaches for controlling robotic systems typically rely heavily on accurately
estimating the full state of the robot (e.g., a running robot might estimate
joint angles and velocities, torso state, and position relative to a goal).
However, full state representations are often excessively rich for the specific
task at hand and can lead to significant computational inefficiency and
brittleness to errors in state estimation. In contrast, we present an approach
that eschews such rich representations and seeks to create task-driven
representations. The key technical insight is to leverage the theory of
information bottlenecks}to formalize the notion of a "task-driven
representation" in terms of information theoretic quantities that measure the
minimality of a representation. We propose novel iterative algorithms for
automatically synthesizing (offline) a task-driven representation (given in
terms of a set of task-relevant variables (TRVs)) and a performant control
policy that is a function of the TRVs. We present online algorithms for
estimating the TRVs in order to apply the control policy. We demonstrate that
our approach results in significant robustness to unmodeled measurement
uncertainty both theoretically and via thorough simulation experiments
including a spring-loaded inverted pendulum running to a goal location.Comment: 9 pages, 4 figures, abridged version accepted to ICRA2019;
Incorporates changes in final conference submissio
Universal Reinforcement Learning Algorithms: Survey and Experiments
Many state-of-the-art reinforcement learning (RL) algorithms typically assume
that the environment is an ergodic Markov Decision Process (MDP). In contrast,
the field of universal reinforcement learning (URL) is concerned with
algorithms that make as few assumptions as possible about the environment. The
universal Bayesian agent AIXI and a family of related URL algorithms have been
developed in this setting. While numerous theoretical optimality results have
been proven for these agents, there has been no empirical investigation of
their behavior to date. We present a short and accessible survey of these URL
algorithms under a unified notation and framework, along with results of some
experiments that qualitatively illustrate some properties of the resulting
policies, and their relative performance on partially-observable gridworld
environments. We also present an open-source reference implementation of the
algorithms which we hope will facilitate further understanding of, and
experimentation with, these ideas.Comment: 8 pages, 6 figures, Twenty-sixth International Joint Conference on
Artificial Intelligence (IJCAI-17
Private Pareto Optimal Exchange
We consider the problem of implementing an individually rational,
asymptotically Pareto optimal allocation in a barter-exchange economy where
agents are endowed with goods and have preferences over the goods of others,
but may not use money as a medium of exchange. Because one of the most
important instantiations of such economies is kidney exchange -- where the
"input"to the problem consists of sensitive patient medical records -- we ask
to what extent such exchanges can be carried out while providing formal privacy
guarantees to the participants. We show that individually rational allocations
cannot achieve any non-trivial approximation to Pareto optimality if carried
out under the constraint of differential privacy -- or even the relaxation of
\emph{joint} differential privacy, under which it is known that asymptotically
optimal allocations can be computed in two-sided markets, where there is a
distinction between buyers and sellers and we are concerned only with privacy
of the buyers~\citep{Matching}. We therefore consider a further relaxation that
we call \emph{marginal} differential privacy -- which promises, informally,
that the privacy of every agent is protected from every other agent so long as does not collude or share allocation information with other
agents. We show that, under marginal differential privacy, it is possible to
compute an individually rational and asymptotically Pareto optimal allocation
in such exchange economies
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